Managing Operational Efficiency and Reducing Aircraft Downtime by Optimization of Aircraft On-Ground (AOG) Processes for Air Operator
This research aims to identify patterns and root causes of aircraft downtimes by comparing various forecasting models used in the aviation industry to prevent AOG events effectively. At its heart, this study explores innovative forecasting models using time series analysis, time series modeling and...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-05-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/9/5129 |
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| author | Iyad Alomar Diallo Nikita |
| author_facet | Iyad Alomar Diallo Nikita |
| author_sort | Iyad Alomar |
| collection | DOAJ |
| description | This research aims to identify patterns and root causes of aircraft downtimes by comparing various forecasting models used in the aviation industry to prevent AOG events effectively. At its heart, this study explores innovative forecasting models using time series analysis, time series modeling and binary classification to predict spare part usage, reduce downtime, and tackle the complexities of managing inventory for diverse aircraft fleets. By analyzing both data and insights shared by aviation industry experts, the research offers a practical roadmap for enhancing supply chain efficiency and reducing Mean Time Between Failures (MTBF). The thesis emphasizes how real-time data integration and hybrid forecasting approaches can transform operations, helping airlines keep spare parts available when and where they are needed most. It also shows how precise forecasting is not just about saving costs, it is about boosting customer satisfaction and staying competitive in an ever-demanding industry. In addition to data-driven insights, this research provides actionable recommendations, such as embracing predictive maintenance strategies and streamlining logistics. These steps aim to ensure smoother operations, fewer disruptions, and more reliable service for passengers and operators alike. |
| format | Article |
| id | doaj-art-e725b5b9f5604cbfb5d7d9449c772626 |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-e725b5b9f5604cbfb5d7d9449c7726262025-08-20T03:49:22ZengMDPI AGApplied Sciences2076-34172025-05-01159512910.3390/app15095129Managing Operational Efficiency and Reducing Aircraft Downtime by Optimization of Aircraft On-Ground (AOG) Processes for Air OperatorIyad Alomar0Diallo Nikita1Transport and Telecommunication Institute, Lauvas 2, LV1019 Riga, LatviaTransport and Telecommunication Institute, Lauvas 2, LV1019 Riga, LatviaThis research aims to identify patterns and root causes of aircraft downtimes by comparing various forecasting models used in the aviation industry to prevent AOG events effectively. At its heart, this study explores innovative forecasting models using time series analysis, time series modeling and binary classification to predict spare part usage, reduce downtime, and tackle the complexities of managing inventory for diverse aircraft fleets. By analyzing both data and insights shared by aviation industry experts, the research offers a practical roadmap for enhancing supply chain efficiency and reducing Mean Time Between Failures (MTBF). The thesis emphasizes how real-time data integration and hybrid forecasting approaches can transform operations, helping airlines keep spare parts available when and where they are needed most. It also shows how precise forecasting is not just about saving costs, it is about boosting customer satisfaction and staying competitive in an ever-demanding industry. In addition to data-driven insights, this research provides actionable recommendations, such as embracing predictive maintenance strategies and streamlining logistics. These steps aim to ensure smoother operations, fewer disruptions, and more reliable service for passengers and operators alike.https://www.mdpi.com/2076-3417/15/9/5129spare parts forecastingAOGaircraft downtime |
| spellingShingle | Iyad Alomar Diallo Nikita Managing Operational Efficiency and Reducing Aircraft Downtime by Optimization of Aircraft On-Ground (AOG) Processes for Air Operator Applied Sciences spare parts forecasting AOG aircraft downtime |
| title | Managing Operational Efficiency and Reducing Aircraft Downtime by Optimization of Aircraft On-Ground (AOG) Processes for Air Operator |
| title_full | Managing Operational Efficiency and Reducing Aircraft Downtime by Optimization of Aircraft On-Ground (AOG) Processes for Air Operator |
| title_fullStr | Managing Operational Efficiency and Reducing Aircraft Downtime by Optimization of Aircraft On-Ground (AOG) Processes for Air Operator |
| title_full_unstemmed | Managing Operational Efficiency and Reducing Aircraft Downtime by Optimization of Aircraft On-Ground (AOG) Processes for Air Operator |
| title_short | Managing Operational Efficiency and Reducing Aircraft Downtime by Optimization of Aircraft On-Ground (AOG) Processes for Air Operator |
| title_sort | managing operational efficiency and reducing aircraft downtime by optimization of aircraft on ground aog processes for air operator |
| topic | spare parts forecasting AOG aircraft downtime |
| url | https://www.mdpi.com/2076-3417/15/9/5129 |
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